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AI Product Case Studies Deep Dive

Study the product decisions behind the most impactful AI products. Each module focuses on a different product, extracting lessons about growth, integration, design, and strategy that you can apply to your own work.

3 modules13 steps~3 hours
0% complete0/13 steps
Path Progress: 0% (0/13 steps)
Module 1: ChatGPT: Growth and Conversational Design0/5
Module 2: AI Integration: GitHub Copilot and Notion AI0/4
Module 3: AI Design Lessons: Figma and Beyond0/4

After completing this path, you will be able to:

  • Analyze what made specific AI products succeed or fail
  • Extract reusable patterns from real AI product launches
  • Apply lessons from ChatGPT, GitHub Copilot, Notion AI, and Figma AI to your own products
  • Compare different approaches to AI product integration

Learning Objectives

  • Understand the product decisions that drove ChatGPT's explosive growth
  • Analyze the conversational UX decisions behind the interface
  • Extract lessons about AI product-led growth
📖
ChatGPT: 100M Users in Two Months
10m
📖
ChatGPT's Conversational Design
10m
🧮
AI Feature Triage
8m
✏️
15m
🏁
5m

Learning Objectives

  • Compare "AI-native" vs. "AI-integrated" product strategies
  • Understand the trade-offs of deep vs. surface-level AI integration
  • Design an AI integration approach for an existing product
📖
GitHub Copilot: The First AI Coding Tool
10m
📖
Notion AI: Integration Without Disruption
10m
✏️
15m
🏁
5m

Learning Objectives

  • Understand how Figma introduced AI to a skeptical design community
  • Extract design principles from successful AI product launches
  • Apply cross-case lessons to your own product
📖
Figma AI: Design Community Integration
10m
📖
Midjourney: Community-Driven AI
10m
✏️
15m
🏁
5m